CBTI Summary

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Table

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isi

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict isi with group and time_point (formula: isi ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to group = control and time_point = 1st , is at 13.53 (95% CI [12.97, 14.09], t(848) = 47.35, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.92, 0.66], t(848) = -0.32, p = 0.751; Std. beta = -0.03, 95% CI [-0.21, 0.15])
  • The effect of time point [2nd] is statistically significant and negative (beta = -2.46, 95% CI [-3.09, -1.82], t(848) = -7.61, p < .001; Std. beta = -0.55, 95% CI [-0.69, -0.41])
  • The effect of time point [3rd] is statistically significant and negative (beta = -2.85, 95% CI [-3.50, -2.20], t(848) = -8.56, p < .001; Std. beta = -0.63, 95% CI [-0.78, -0.49])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.92, -2.01], t(848) = -6.09, p < .001; Std. beta = -0.66, 95% CI [-0.87, -0.45])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.99, 95% CI [-3.96, -2.01], t(848) = -6.01, p < .001; Std. beta = -0.67, 95% CI [-0.88, -0.45])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st , is at 9.82 (95% CI [9.22, 10.42], t(848) = 32.16, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.68, 1.01], t(848) = 0.38, p = 0.708; Std. beta = 0.04, 95% CI [-0.16, 0.24])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.73, 95% CI [0.14, 1.32], t(848) = 2.44, p = 0.015; Std. beta = 0.17, 95% CI [0.03, 0.31])
  • The effect of time point [3rd] is statistically significant and positive (beta = 0.92, 95% CI [0.32, 1.52], t(848) = 2.98, p = 0.003; Std. beta = 0.22, 95% CI [0.07, 0.36])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.40, 95% CI [0.51, 2.29], t(848) = 3.09, p = 0.002; Std. beta = 0.33, 95% CI [0.12, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 1.64, 95% CI [0.73, 2.55], t(848) = 3.54, p < .001; Std. beta = 0.39, 95% CI [0.17, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st , is at 8.21 (95% CI [7.47, 8.95], t(848) = 21.69, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.59, 95% CI [-0.46, 1.64], t(848) = 1.11, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.32])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.78, 95% CI [-1.43, -0.13], t(848) = -2.34, p = 0.019; Std. beta = -0.15, 95% CI [-0.28, -0.02])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.63, 95% CI [-1.31, 0.04], t(848) = -1.84, p = 0.065; Std. beta = -0.12, 95% CI [-0.25, 7.65e-03])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.73, 95% CI [-2.73, -0.74], t(848) = -3.42, p < .001; Std. beta = -0.33, 95% CI [-0.52, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.45, 95% CI [-3.46, -1.44], t(848) = -4.74, p < .001; Std. beta = -0.47, 95% CI [-0.67, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st , is at 7.54 (95% CI [6.79, 8.28], t(848) = 19.74, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.48, 95% CI [-0.58, 1.54], t(848) = 0.89, p = 0.373; Std. beta = 0.09, 95% CI [-0.11, 0.30])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.11, 0.23], t(848) = -1.29, p = 0.198; Std. beta = -0.08, 95% CI [-0.21, 0.04])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.60, 95% CI [-1.28, 0.09], t(848) = -1.69, p = 0.091; Std. beta = -0.11, 95% CI [-0.25, 0.02])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.07, 95% CI [-3.09, -1.06], t(848) = -4.00, p < .001; Std. beta = -0.40, 95% CI [-0.60, -0.20])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.37, 95% CI [-3.41, -1.33], t(848) = -4.47, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

wsas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas with group and time_point (formula: wsas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st , is at 16.77 (95% CI [15.30, 18.24], t(848) = 22.41, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-2.16, 1.99], t(848) = -0.08, p = 0.937; Std. beta = -8.24e-03, 95% CI [-0.21, 0.20])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.18, 0.54], t(848) = -1.18, p = 0.239; Std. beta = -0.08, 95% CI [-0.21, 0.05])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.09, 95% CI [-1.49, 1.32], t(848) = -0.12, p = 0.902; Std. beta = -8.73e-03, 95% CI [-0.15, 0.13])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.95, 95% CI [-5.02, -0.88], t(848) = -2.80, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -4.95, 95% CI [-7.06, -2.83], t(848) = -4.59, p < .001; Std. beta = -0.49, 95% CI [-0.69, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_arousal

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_arousal with group and time_point (formula: shps_arousal ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st , is at 3.02 (95% CI [2.91, 3.13], t(848) = 54.46, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.16, 95% CI [8.83e-03, 0.32], t(848) = 2.07, p = 0.038; Std. beta = 0.21, 95% CI [0.01, 0.40])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.31, -0.08], t(848) = -3.30, p < .001; Std. beta = -0.25, 95% CI [-0.39, -0.10])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.22, 95% CI [-0.34, -0.10], t(848) = -3.58, p < .001; Std. beta = -0.28, 95% CI [-0.43, -0.13])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.48, 95% CI [-0.65, -0.30], t(848) = -5.33, p < .001; Std. beta = -0.60, 95% CI [-0.83, -0.38])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.57, 95% CI [-0.74, -0.39], t(848) = -6.18, p < .001; Std. beta = -0.72, 95% CI [-0.94, -0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_schedule

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_schedule with group and time_point (formula: shps_schedule ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st , is at 3.53 (95% CI [3.40, 3.66], t(848) = 53.11, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.14, 0.23], t(848) = 0.44, p = 0.659; Std. beta = 0.05, 95% CI [-0.16, 0.25])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.22, 0.02], t(848) = -1.68, p = 0.093; Std. beta = -0.11, 95% CI [-0.24, 0.02])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.13, 95% CI [-0.25, -0.01], t(848) = -2.14, p = 0.033; Std. beta = -0.14, 95% CI [-0.28, -0.01])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.52, -0.17], t(848) = -3.79, p < .001; Std. beta = -0.38, 95% CI [-0.57, -0.18])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.61, -0.24], t(848) = -4.57, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_behavior

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_behavior with group and time_point (formula: shps_behavior ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.99 (95% CI [1.89, 2.08], t(848) = 39.00, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.13, 95% CI [-8.87e-03, 0.27], t(848) = 1.84, p = 0.066; Std. beta = 0.19, 95% CI [-0.01, 0.40])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.12], t(848) = 0.48, p = 0.629; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 9.23e-03, 95% CI [-0.09, 0.11], t(848) = 0.18, p = 0.860; Std. beta = 0.01, 95% CI [-0.14, 0.16])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.09], t(848) = -3.18, p = 0.001; Std. beta = -0.35, 95% CI [-0.57, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.33, 95% CI [-0.49, -0.18], t(848) = -4.25, p < .001; Std. beta = -0.48, 95% CI [-0.71, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_environment

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_environment with group and time_point (formula: shps_environment ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 2.33 (95% CI [2.21, 2.45], t(848) = 38.44, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.23, 0.11], t(848) = -0.72, p = 0.469; Std. beta = -0.08, 95% CI [-0.28, 0.13])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(848) = -0.97, p = 0.331; Std. beta = -0.07, 95% CI [-0.22, 0.07])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(848) = -0.99, p = 0.322; Std. beta = -0.08, 95% CI [-0.22, 0.07])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.26, 0.09], t(848) = -0.94, p = 0.347; Std. beta = -0.10, 95% CI [-0.32, 0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.26, 95% CI [-0.44, -0.08], t(848) = -2.78, p = 0.005; Std. beta = -0.32, 95% CI [-0.54, -0.09])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_consequence

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_consequence with group and time_point (formula: dbas_consequence ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to group = control and time_point = 1st , is at 6.59 (95% CI [6.31, 6.86], t(848) = 46.90, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.34, 0.44], t(848) = 0.27, p = 0.787; Std. beta = 0.03, 95% CI [-0.17, 0.22])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.34, 95% CI [-0.61, -0.06], t(848) = -2.39, p = 0.017; Std. beta = -0.17, 95% CI [-0.30, -0.03])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.67, 95% CI [-0.95, -0.38], t(848) = -4.61, p < .001; Std. beta = -0.33, 95% CI [-0.47, -0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.11, 95% CI [-1.53, -0.69], t(848) = -5.22, p < .001; Std. beta = -0.55, 95% CI [-0.76, -0.34])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.30, 95% CI [-1.73, -0.87], t(848) = -5.98, p < .001; Std. beta = -0.65, 95% CI [-0.86, -0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_worry

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_worry with group and time_point (formula: dbas_worry ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to group = control and time_point = 1st , is at 14.20 (95% CI [13.65, 14.76], t(848) = 50.06, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.45, 1.13], t(848) = 0.85, p = 0.396; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.23, 95% CI [-1.86, -0.60], t(848) = -3.81, p < .001; Std. beta = -0.30, 95% CI [-0.45, -0.14])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.84, 95% CI [-2.50, -1.19], t(848) = -5.54, p < .001; Std. beta = -0.44, 95% CI [-0.60, -0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.71, 95% CI [-3.67, -1.76], t(848) = -5.57, p < .001; Std. beta = -0.65, 95% CI [-0.88, -0.42])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.87, 95% CI [-3.84, -1.90], t(848) = -5.77, p < .001; Std. beta = -0.69, 95% CI [-0.93, -0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_expectation

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_expectation with group and time_point (formula: dbas_expectation ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st , is at 7.17 (95% CI [6.84, 7.51], t(848) = 41.62, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.28, 95% CI [-0.76, 0.19], t(848) = -1.17, p = 0.242; Std. beta = -0.12, 95% CI [-0.31, 0.08])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-0.69, 1.84e-03], t(848) = -1.95, p = 0.051; Std. beta = -0.14, 95% CI [-0.28, 7.53e-04])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.77, 95% CI [-1.13, -0.42], t(848) = -4.26, p < .001; Std. beta = -0.32, 95% CI [-0.46, -0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.25, 95% CI [-1.77, -0.73], t(848) = -4.69, p < .001; Std. beta = -0.51, 95% CI [-0.72, -0.30])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.28, 95% CI [-1.82, -0.75], t(848) = -4.72, p < .001; Std. beta = -0.53, 95% CI [-0.74, -0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_medication

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_medication with group and time_point (formula: dbas_medication ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st , is at 3.15 (95% CI [2.83, 3.46], t(848) = 19.54, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.36, 0.54], t(848) = 0.39, p = 0.695; Std. beta = 0.04, 95% CI [-0.17, 0.25])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.37, 95% CI [0.04, 0.69], t(848) = 2.23, p = 0.026; Std. beta = 0.17, 95% CI [0.02, 0.32])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.02, 0.64], t(848) = 1.83, p = 0.068; Std. beta = 0.14, 95% CI [-0.01, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.66, 95% CI [-1.15, -0.18], t(848) = -2.67, p = 0.008; Std. beta = -0.31, 95% CI [-0.53, -0.08])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.86, 95% CI [-1.36, -0.36], t(848) = -3.39, p < .001; Std. beta = -0.40, 95% CI [-0.63, -0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_somatic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_somatic with group and time_point (formula: psas_somatic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.86 (95% CI [1.76, 1.96], t(848) = 36.72, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.10, 0.19], t(848) = 0.62, p = 0.533; Std. beta = 0.07, 95% CI [-0.14, 0.27])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.14, 95% CI [0.05, 0.24], t(848) = 3.03, p = 0.002; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 5.72e-03, 95% CI [-0.09, 0.10], t(848) = 0.12, p = 0.907; Std. beta = 8.37e-03, 95% CI [-0.13, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.17], t(848) = -4.27, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.24])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.38, -0.09], t(848) = -3.24, p = 0.001; Std. beta = -0.35, 95% CI [-0.56, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_cognitive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_cognitive with group and time_point (formula: psas_cognitive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to group = control and time_point = 1st , is at 2.87 (95% CI [2.75, 3.00], t(848) = 45.24, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.08, 0.28], t(848) = 1.10, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.31])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.33, -0.08], t(848) = -3.20, p = 0.001; Std. beta = -0.23, 95% CI [-0.37, -0.09])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.36, 95% CI [-0.49, -0.23], t(848) = -5.52, p < .001; Std. beta = -0.41, 95% CI [-0.55, -0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.62, -0.24], t(848) = -4.49, p < .001; Std. beta = -0.49, 95% CI [-0.70, -0.28])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.41, 95% CI [-0.60, -0.21], t(848) = -4.12, p < .001; Std. beta = -0.46, 95% CI [-0.67, -0.24])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psqi_global

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psqi_global with group and time_point (formula: psqi_global ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to group = control and time_point = 1st , is at 10.72 (95% CI [10.26, 11.19], t(848) = 45.23, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-0.37, 0.95], t(848) = 0.87, p = 0.386; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.31, 95% CI [-1.82, -0.81], t(848) = -5.09, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.31, 95% CI [-1.84, -0.79], t(848) = -4.95, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.86, 95% CI [-2.62, -1.10], t(848) = -4.78, p < .001; Std. beta = -0.54, 95% CI [-0.76, -0.32])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.44, 95% CI [-3.22, -1.66], t(848) = -6.15, p < .001; Std. beta = -0.71, 95% CI [-0.93, -0.48])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_attention

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_attention with group and time_point (formula: mic_attention ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.30 (95% CI [1.19, 1.41], t(848) = 22.91, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.04, 0.28], t(848) = 1.52, p = 0.129; Std. beta = 0.16, 95% CI [-0.05, 0.36])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.13, 0.09], t(848) = -0.39, p = 0.694; Std. beta = -0.03, 95% CI [-0.17, 0.11])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.08, 0.15], t(848) = 0.59, p = 0.558; Std. beta = 0.04, 95% CI [-0.10, 0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.25, 95% CI [-0.41, -0.08], t(848) = -2.95, p = 0.003; Std. beta = -0.32, 95% CI [-0.54, -0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.39, 95% CI [-0.56, -0.22], t(848) = -4.51, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_executive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_executive with group and time_point (formula: mic_executive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.28 (95% CI [1.17, 1.39], t(848) = 22.00, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.23], t(848) = 0.82, p = 0.415; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.14, 0.07], t(848) = -0.62, p = 0.537; Std. beta = -0.04, 95% CI [-0.18, 0.09])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.16, 0.06], t(848) = -0.90, p = 0.368; Std. beta = -0.06, 95% CI [-0.20, 0.08])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.32, 2.51e-03], t(848) = -1.93, p = 0.054; Std. beta = -0.20, 95% CI [-0.41, 3.18e-03])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.27, 95% CI [-0.44, -0.10], t(848) = -3.20, p = 0.001; Std. beta = -0.34, 95% CI [-0.55, -0.13])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_memory

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_memory with group and time_point (formula: mic_memory ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 1.33 (95% CI [1.22, 1.44], t(848) = 23.30, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.22], t(848) = 0.81, p = 0.417; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.07, 0.13], t(848) = 0.62, p = 0.538; Std. beta = 0.04, 95% CI [-0.09, 0.17])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.07, 95% CI [-0.17, 0.04], t(848) = -1.23, p = 0.217; Std. beta = -0.08, 95% CI [-0.22, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.28, 95% CI [-0.43, -0.12], t(848) = -3.55, p < .001; Std. beta = -0.35, 95% CI [-0.55, -0.16])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.22, 95% CI [-0.37, -0.06], t(848) = -2.75, p = 0.006; Std. beta = -0.28, 95% CI [-0.48, -0.08])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st , is at 46.33 (95% CI [45.04, 47.63], t(848) = 70.36, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.96, 1.69], t(848) = -0.15, p = 0.881; Std. beta = -0.02, 95% CI [-0.22, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.87, 95% CI [-2.03, 0.29], t(848) = -1.48, p = 0.140; Std. beta = -0.10, 95% CI [-0.23, 0.03])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.79, 95% CI [-1.99, 0.40], t(848) = -1.30, p = 0.194; Std. beta = -0.09, 95% CI [-0.22, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 2.76, 95% CI [1.00, 4.52], t(848) = 3.08, p = 0.002; Std. beta = 0.31, 95% CI [0.11, 0.51])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 3.21, 95% CI [1.41, 5.00], t(848) = 3.50, p < .001; Std. beta = 0.36, 95% CI [0.16, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st , is at 39.90 (95% CI [38.39, 41.41], t(848) = 51.75, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-2.05, 2.22], t(848) = 0.08, p = 0.938; Std. beta = 7.97e-03, 95% CI [-0.19, 0.21])
  • The effect of time point [2nd] is statistically significant and positive (beta = 2.00, 95% CI [0.55, 3.45], t(848) = 2.71, p = 0.007; Std. beta = 0.19, 95% CI [0.05, 0.32])
  • The effect of time point [3rd] is statistically significant and positive (beta = 2.26, 95% CI [0.76, 3.76], t(848) = 2.96, p = 0.003; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 3.57, 95% CI [1.37, 5.77], t(848) = 3.18, p = 0.001; Std. beta = 0.33, 95% CI [0.13, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 4.67, 95% CI [2.43, 6.92], t(848) = 4.08, p < .001; Std. beta = 0.44, 95% CI [0.23, 0.65])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

Post hoc analysis

Table

Between group

isi

1st

t(640.87) = -0.32, p = 0.751, Cohen d = 0.05, 95% CI (-0.92 to 0.67)

2st

t(756.19) = -6.60, p = 0.000, Cohen d = 1.09, 95% CI (-4.01 to -2.17)

3rd

t(774.11) = -6.51, p = 0.000, Cohen d = 1.09, 95% CI (-4.06 to -2.18)

who

1st

t(548.22) = 0.38, p = 0.708, Cohen d = -0.06, 95% CI (-0.69 to 1.01)

2st

t(686.86) = 3.20, p = 0.001, Cohen d = -0.60, 95% CI (0.60 to 2.52)

3rd

t(708.71) = 3.62, p = 0.000, Cohen d = -0.69, 95% CI (0.82 to 2.78)

phq

1st

t(500.79) = 1.11, p = 0.269, Cohen d = -0.20, 95% CI (-0.46 to 1.64)

2st

t(636.30) = -1.92, p = 0.055, Cohen d = 0.39, 95% CI (-2.31 to 0.03)

3rd

t(657.97) = -3.07, p = 0.002, Cohen d = 0.64, 95% CI (-3.04 to -0.67)

gad

1st

t(506.80) = 0.89, p = 0.374, Cohen d = -0.16, 95% CI (-0.58 to 1.54)

2st

t(643.49) = -2.65, p = 0.008, Cohen d = 0.53, 95% CI (-2.77 to -0.41)

3rd

t(665.30) = -3.09, p = 0.002, Cohen d = 0.63, 95% CI (-3.09 to -0.69)

wsas

1st

t(523.00) = -0.08, p = 0.937, Cohen d = 0.01, 95% CI (-2.16 to 2.00)

2st

t(661.66) = -2.56, p = 0.011, Cohen d = 0.50, 95% CI (-5.37 to -0.71)

3rd

t(683.68) = -4.17, p = 0.000, Cohen d = 0.83, 95% CI (-7.40 to -2.66)

shps_arousal

1st

t(599.94) = 2.07, p = 0.039, Cohen d = -0.31, 95% CI (0.01 to 0.32)

2st

t(729.24) = -3.49, p = 0.001, Cohen d = 0.60, 95% CI (-0.49 to -0.14)

3rd

t(749.39) = -4.38, p = 0.000, Cohen d = 0.77, 95% CI (-0.58 to -0.22)

shps_schedule

1st

t(510.09) = 0.44, p = 0.659, Cohen d = -0.08, 95% CI (-0.14 to 0.23)

2st

t(647.31) = -2.89, p = 0.004, Cohen d = 0.58, 95% CI (-0.51 to -0.10)

3rd

t(669.19) = -3.60, p = 0.000, Cohen d = 0.73, 95% CI (-0.59 to -0.17)

shps_behavior

1st

t(556.53) = 1.84, p = 0.067, Cohen d = -0.30, 95% CI (-0.01 to 0.27)

2st

t(694.44) = -1.37, p = 0.172, Cohen d = 0.25, 95% CI (-0.27 to 0.05)

3rd

t(716.13) = -2.41, p = 0.016, Cohen d = 0.45, 95% CI (-0.36 to -0.04)

shps_environment

1st

t(552.62) = -0.72, p = 0.469, Cohen d = 0.12, 95% CI (-0.23 to 0.11)

2st

t(690.92) = -1.52, p = 0.129, Cohen d = 0.28, 95% CI (-0.34 to 0.04)

3rd

t(712.69) = -3.23, p = 0.001, Cohen d = 0.61, 95% CI (-0.51 to -0.13)

dbas_consequence

1st

t(560.03) = 0.27, p = 0.787, Cohen d = -0.04, 95% CI (-0.34 to 0.44)

2st

t(697.54) = -4.69, p = 0.000, Cohen d = 0.86, 95% CI (-1.50 to -0.61)

3rd

t(719.15) = -5.42, p = 0.000, Cohen d = 1.01, 95% CI (-1.70 to -0.79)

dbas_worry

1st

t(647.46) = 0.85, p = 0.396, Cohen d = -0.12, 95% CI (-0.45 to 1.13)

2st

t(760.10) = -5.10, p = 0.000, Cohen d = 0.83, 95% CI (-3.29 to -1.46)

3rd

t(777.61) = -5.31, p = 0.000, Cohen d = 0.89, 95% CI (-3.46 to -1.59)

dbas_expectation

1st

t(571.34) = -1.17, p = 0.243, Cohen d = 0.18, 95% CI (-0.76 to 0.19)

2st

t(707.17) = -5.52, p = 0.000, Cohen d = 0.99, 95% CI (-2.08 to -0.99)

3rd

t(728.46) = -5.54, p = 0.000, Cohen d = 1.02, 95% CI (-2.12 to -1.01)

dbas_medication

1st

t(571.27) = 0.39, p = 0.695, Cohen d = -0.06, 95% CI (-0.36 to 0.54)

2st

t(707.11) = -2.21, p = 0.027, Cohen d = 0.40, 95% CI (-1.08 to -0.07)

3rd

t(728.40) = -2.92, p = 0.004, Cohen d = 0.53, 95% CI (-1.29 to -0.25)

psas_somatic

1st

t(524.92) = 0.62, p = 0.533, Cohen d = -0.11, 95% CI (-0.10 to 0.19)

2st

t(663.70) = -3.25, p = 0.001, Cohen d = 0.63, 95% CI (-0.42 to -0.10)

3rd

t(685.73) = -2.36, p = 0.019, Cohen d = 0.47, 95% CI (-0.35 to -0.03)

psas_cognitive

1st

t(562.06) = 1.10, p = 0.270, Cohen d = -0.18, 95% CI (-0.08 to 0.28)

2st

t(699.31) = -3.28, p = 0.001, Cohen d = 0.60, 95% CI (-0.54 to -0.13)

3rd

t(720.87) = -2.96, p = 0.003, Cohen d = 0.55, 95% CI (-0.51 to -0.10)

psqi_global

1st

t(612.85) = 0.87, p = 0.386, Cohen d = -0.13, 95% CI (-0.37 to 0.95)

2st

t(738.27) = -4.07, p = 0.000, Cohen d = 0.69, 95% CI (-2.33 to -0.81)

3rd

t(757.79) = -5.46, p = 0.000, Cohen d = 0.95, 95% CI (-2.93 to -1.38)

mic_attention

1st

t(548.32) = 1.52, p = 0.130, Cohen d = -0.25, 95% CI (-0.04 to 0.28)

2st

t(686.95) = -1.39, p = 0.165, Cohen d = 0.26, 95% CI (-0.30 to 0.05)

3rd

t(708.81) = -2.88, p = 0.004, Cohen d = 0.55, 95% CI (-0.45 to -0.08)

mic_executive

1st

t(526.28) = 0.82, p = 0.415, Cohen d = -0.14, 95% CI (-0.09 to 0.23)

2st

t(665.14) = -1.00, p = 0.318, Cohen d = 0.19, 95% CI (-0.27 to 0.09)

3rd

t(687.17) = -2.16, p = 0.031, Cohen d = 0.43, 95% CI (-0.39 to -0.02)

mic_memory

1st

t(506.60) = 0.81, p = 0.417, Cohen d = -0.15, 95% CI (-0.09 to 0.22)

2st

t(643.25) = -2.33, p = 0.020, Cohen d = 0.47, 95% CI (-0.39 to -0.03)

3rd

t(665.06) = -1.66, p = 0.097, Cohen d = 0.34, 95% CI (-0.33 to 0.03)

nb_pcs

1st

t(508.06) = -0.15, p = 0.882, Cohen d = 0.03, 95% CI (-1.97 to 1.69)

2st

t(644.96) = 2.52, p = 0.012, Cohen d = -0.51, 95% CI (0.58 to 4.66)

3rd

t(666.80) = 2.91, p = 0.004, Cohen d = -0.60, 95% CI (1.00 to 5.14)

nb_mcs

1st

t(538.10) = 0.08, p = 0.938, Cohen d = -0.01, 95% CI (-2.06 to 2.23)

2st

t(677.16) = 2.97, p = 0.003, Cohen d = -0.56, 95% CI (1.24 to 6.07)

3rd

t(699.15) = 3.80, p = 0.000, Cohen d = -0.73, 95% CI (2.30 to 7.21)

Within treatment group

isi

1st vs 2st

t(594.74) = -14.88, p = 0.000, Cohen d = 1.90, 95% CI (-6.14 to -4.71)

1st vs 3rd

t(596.42) = -15.80, p = 0.000, Cohen d = 2.05, 95% CI (-6.56 to -5.11)

who

1st vs 2st

t(571.40) = 6.26, p = 0.000, Cohen d = -0.81, 95% CI (1.46 to 2.80)

1st vs 3rd

t(572.14) = 7.41, p = 0.000, Cohen d = -0.98, 95% CI (1.88 to 3.24)

phq

1st vs 2st

t(556.90) = -6.59, p = 0.000, Cohen d = 0.86, 95% CI (-3.26 to -1.76)

1st vs 3rd

t(557.28) = -7.98, p = 0.000, Cohen d = 1.06, 95% CI (-3.84 to -2.32)

gad

1st vs 2st

t(558.86) = -6.44, p = 0.000, Cohen d = 0.84, 95% CI (-3.28 to -1.75)

1st vs 3rd

t(559.28) = -7.49, p = 0.000, Cohen d = 0.99, 95% CI (-3.74 to -2.19)

wsas

1st vs 2st

t(563.96) = -4.75, p = 0.000, Cohen d = 0.62, 95% CI (-5.33 to -2.21)

1st vs 3rd

t(564.49) = -6.25, p = 0.000, Cohen d = 0.83, 95% CI (-6.62 to -3.45)

shps_arousal

1st vs 2st

t(585.10) = -10.02, p = 0.000, Cohen d = 1.29, 95% CI (-0.80 to -0.54)

1st vs 3rd

t(586.33) = -11.51, p = 0.000, Cohen d = 1.50, 95% CI (-0.92 to -0.65)

shps_schedule

1st vs 2st

t(559.92) = -6.50, p = 0.000, Cohen d = 0.85, 95% CI (-0.58 to -0.31)

1st vs 3rd

t(560.36) = -8.01, p = 0.000, Cohen d = 1.06, 95% CI (-0.69 to -0.42)

shps_behavior

1st vs 2st

t(573.74) = -3.81, p = 0.000, Cohen d = 0.49, 95% CI (-0.33 to -0.11)

1st vs 3rd

t(574.55) = -5.54, p = 0.000, Cohen d = 0.73, 95% CI (-0.44 to -0.21)

shps_environment

1st vs 2st

t(572.65) = -2.11, p = 0.071, Cohen d = 0.27, 95% CI (-0.28 to -0.01)

1st vs 3rd

t(573.42) = -4.61, p = 0.000, Cohen d = 0.61, 95% CI (-0.45 to -0.18)

dbas_consequence

1st vs 2st

t(574.71) = -9.05, p = 0.000, Cohen d = 1.17, 95% CI (-1.76 to -1.13)

1st vs 3rd

t(575.54) = -12.14, p = 0.000, Cohen d = 1.60, 95% CI (-2.29 to -1.65)

dbas_worry

1st vs 2st

t(596.20) = -10.82, p = 0.000, Cohen d = 1.38, 95% CI (-4.66 to -3.23)

1st vs 3rd

t(597.96) = -12.76, p = 0.000, Cohen d = 1.65, 95% CI (-5.44 to -3.99)

dbas_expectation

1st vs 2st

t(577.77) = -7.96, p = 0.000, Cohen d = 1.03, 95% CI (-1.98 to -1.20)

1st vs 3rd

t(578.71) = -10.14, p = 0.000, Cohen d = 1.33, 95% CI (-2.45 to -1.66)

dbas_medication

1st vs 2st

t(577.75) = -1.59, p = 0.223, Cohen d = 0.21, 95% CI (-0.66 to 0.07)

1st vs 3rd

t(578.69) = -2.91, p = 0.008, Cohen d = 0.38, 95% CI (-0.92 to -0.18)

psas_somatic

1st vs 2st

t(564.54) = -3.01, p = 0.005, Cohen d = 0.39, 95% CI (-0.27 to -0.06)

1st vs 3rd

t(565.09) = -4.24, p = 0.000, Cohen d = 0.56, 95% CI (-0.34 to -0.12)

psas_cognitive

1st vs 2st

t(575.27) = -8.80, p = 0.000, Cohen d = 1.14, 95% CI (-0.78 to -0.50)

1st vs 3rd

t(576.12) = -10.47, p = 0.000, Cohen d = 1.38, 95% CI (-0.91 to -0.63)

psqi_global

1st vs 2st

t(588.25) = -10.87, p = 0.000, Cohen d = 1.40, 95% CI (-3.75 to -2.60)

1st vs 3rd

t(589.60) = -12.70, p = 0.000, Cohen d = 1.66, 95% CI (-4.34 to -3.18)

mic_attention

1st vs 2st

t(571.43) = -4.27, p = 0.000, Cohen d = 0.55, 95% CI (-0.39 to -0.15)

1st vs 3rd

t(572.17) = -5.52, p = 0.000, Cohen d = 0.73, 95% CI (-0.48 to -0.23)

mic_executive

1st vs 2st

t(564.96) = -3.11, p = 0.004, Cohen d = 0.41, 95% CI (-0.31 to -0.07)

1st vs 3rd

t(565.52) = -5.09, p = 0.000, Cohen d = 0.67, 95% CI (-0.44 to -0.20)

mic_memory

1st vs 2st

t(558.80) = -4.18, p = 0.000, Cohen d = 0.55, 95% CI (-0.36 to -0.13)

1st vs 3rd

t(559.21) = -4.78, p = 0.000, Cohen d = 0.63, 95% CI (-0.40 to -0.17)

nb_pcs

1st vs 2st

t(559.27) = 2.80, p = 0.011, Cohen d = -0.37, 95% CI (0.56 to 3.21)

1st vs 3rd

t(559.70) = 3.53, p = 0.001, Cohen d = -0.47, 95% CI (1.07 to 3.76)

nb_mcs

1st vs 2st

t(568.48) = 6.61, p = 0.000, Cohen d = -0.86, 95% CI (3.92 to 7.23)

1st vs 3rd

t(569.13) = 8.10, p = 0.000, Cohen d = -1.07, 95% CI (5.25 to 8.61)

Within control group

isi

1st vs 2st

t(541.68) = -7.61, p = 0.000, Cohen d = 0.86, 95% CI (-3.09 to -1.82)

1st vs 3rd

t(548.03) = -8.56, p = 0.000, Cohen d = 1.00, 95% CI (-3.50 to -2.19)

who

1st vs 2st

t(529.52) = 2.44, p = 0.030, Cohen d = -0.28, 95% CI (0.14 to 1.32)

1st vs 3rd

t(533.45) = 2.98, p = 0.006, Cohen d = -0.35, 95% CI (0.31 to 1.53)

phq

1st vs 2st

t(522.50) = -2.34, p = 0.040, Cohen d = 0.27, 95% CI (-1.43 to -0.12)

1st vs 3rd

t(525.31) = -1.84, p = 0.132, Cohen d = 0.22, 95% CI (-1.31 to 0.04)

gad

1st vs 2st

t(523.43) = -1.29, p = 0.396, Cohen d = 0.15, 95% CI (-1.11 to 0.23)

1st vs 3rd

t(526.38) = -1.69, p = 0.183, Cohen d = 0.20, 95% CI (-1.29 to 0.10)

wsas

1st vs 2st

t(525.88) = -1.18, p = 0.479, Cohen d = 0.13, 95% CI (-2.19 to 0.55)

1st vs 3rd

t(529.20) = -0.12, p = 1.000, Cohen d = 0.01, 95% CI (-1.50 to 1.32)

shps_arousal

1st vs 2st

t(536.49) = -3.30, p = 0.002, Cohen d = 0.38, 95% CI (-0.31 to -0.08)

1st vs 3rd

t(541.73) = -3.58, p = 0.001, Cohen d = 0.42, 95% CI (-0.34 to -0.10)

shps_schedule

1st vs 2st

t(523.93) = -1.68, p = 0.188, Cohen d = 0.19, 95% CI (-0.22 to 0.02)

1st vs 3rd

t(526.95) = -2.14, p = 0.066, Cohen d = 0.25, 95% CI (-0.25 to -0.01)

shps_behavior

1st vs 2st

t(530.68) = 0.48, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.12)

1st vs 3rd

t(534.81) = 0.18, p = 1.000, Cohen d = -0.02, 95% CI (-0.09 to 0.11)

shps_environment

1st vs 2st

t(530.14) = -0.97, p = 0.662, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

1st vs 3rd

t(534.17) = -0.99, p = 0.646, Cohen d = 0.12, 95% CI (-0.18 to 0.06)

dbas_consequence

1st vs 2st

t(531.17) = -2.39, p = 0.034, Cohen d = 0.27, 95% CI (-0.61 to -0.06)

1st vs 3rd

t(535.38) = -4.61, p = 0.000, Cohen d = 0.54, 95% CI (-0.95 to -0.38)

dbas_worry

1st vs 2st

t(542.50) = -3.81, p = 0.000, Cohen d = 0.43, 95% CI (-1.87 to -0.60)

1st vs 3rd

t(549.04) = -5.54, p = 0.000, Cohen d = 0.65, 95% CI (-2.50 to -1.19)

dbas_expectation

1st vs 2st

t(532.71) = -1.95, p = 0.104, Cohen d = 0.22, 95% CI (-0.69 to 0.00)

1st vs 3rd

t(537.21) = -4.26, p = 0.000, Cohen d = 0.50, 95% CI (-1.13 to -0.42)

dbas_medication

1st vs 2st

t(532.70) = 2.23, p = 0.053, Cohen d = -0.25, 95% CI (0.04 to 0.69)

1st vs 3rd

t(537.20) = 1.83, p = 0.137, Cohen d = -0.21, 95% CI (-0.02 to 0.64)

psas_somatic

1st vs 2st

t(526.16) = 3.03, p = 0.005, Cohen d = -0.35, 95% CI (0.05 to 0.24)

1st vs 3rd

t(529.53) = 0.12, p = 1.000, Cohen d = -0.01, 95% CI (-0.09 to 0.10)

psas_cognitive

1st vs 2st

t(531.45) = -3.20, p = 0.003, Cohen d = 0.37, 95% CI (-0.33 to -0.08)

1st vs 3rd

t(535.71) = -5.52, p = 0.000, Cohen d = 0.65, 95% CI (-0.49 to -0.23)

psqi_global

1st vs 2st

t(538.16) = -5.09, p = 0.000, Cohen d = 0.58, 95% CI (-1.82 to -0.81)

1st vs 3rd

t(543.73) = -4.95, p = 0.000, Cohen d = 0.58, 95% CI (-1.84 to -0.79)

mic_attention

1st vs 2st

t(529.53) = -0.39, p = 1.000, Cohen d = 0.04, 95% CI (-0.13 to 0.09)

1st vs 3rd

t(533.46) = 0.59, p = 1.000, Cohen d = -0.07, 95% CI (-0.08 to 0.15)

mic_executive

1st vs 2st

t(526.36) = -0.62, p = 1.000, Cohen d = 0.07, 95% CI (-0.14 to 0.07)

1st vs 3rd

t(529.76) = -0.90, p = 0.738, Cohen d = 0.11, 95% CI (-0.16 to 0.06)

mic_memory

1st vs 2st

t(523.40) = 0.62, p = 1.000, Cohen d = -0.07, 95% CI (-0.07 to 0.13)

1st vs 3rd

t(526.34) = -1.23, p = 0.435, Cohen d = 0.15, 95% CI (-0.17 to 0.04)

nb_pcs

1st vs 2st

t(523.62) = -1.47, p = 0.282, Cohen d = 0.17, 95% CI (-2.03 to 0.29)

1st vs 3rd

t(526.60) = -1.30, p = 0.389, Cohen d = 0.15, 95% CI (-1.99 to 0.41)

nb_mcs

1st vs 2st

t(528.08) = 2.71, p = 0.014, Cohen d = -0.31, 95% CI (0.55 to 3.46)

1st vs 3rd

t(531.76) = 2.96, p = 0.006, Cohen d = -0.35, 95% CI (0.76 to 3.76)

Plot

Clinical significance